Data Scientist (Masters)

About The Role

What if your expertise in machine learning, statistical inference, and data engineering could directly shape how the world's most advanced AI systems reason through complex problems? We're looking for Data Scientists with graduate-level training to challenge, audit, and refine cutting-edge AI models — exposing their blind spots and helping harden their reasoning from the inside out.

This is a fully remote, flexible contract role. No prior AI industry experience required — just deep, applied knowledge of data science and a sharp eye for technical precision.

• Organization: Alignerr
• Type: Hourly Contract
• Location: Remote
• Commitment: 10–40 hours/week

• Design Advanced Challenges — Craft complex, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
• Author Ground-Truth Solutions — Develop rigorous, step-by-step technical solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as authoritative reference answers
• Audit AI-Generated Code — Evaluate AI outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and best practices
• Refine AI Reasoning — Identify and document logical failures — such as data leakage, overfitting, or mishandled class imbalances — and provide structured feedback that improves how models think
• Work Independently — Complete task-based assignments asynchronously, fully on your own schedule

• Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong emphasis on data analysis
• Solid foundational knowledge across core areas — supervised/unsupervised learning, deep learning, big data technologies (Spark, Hadoop), or NLP
• Able to communicate highly technical algorithmic and statistical concepts clearly and precisely in writing
• Naturally detail-oriented — you catch errors in code syntax, mathematical notation, and statistical conclusions that others miss
• Self-directed and reliable when working independently without hand-holding
• No prior AI training or annotation experience required

• Prior experience with data annotation, data quality evaluation, or model assessment workflows
• Proficiency in production-level data science practices — MLOps, CI/CD pipelines for models, or model monitoring
• Familiarity with experiment tracking tools (e.g., MLflow, Weights & Biases)
• Broad exposure across multiple data science subfields

• Work directly on frontier AI projects alongside leading research labs and model developers
• Fully remote and flexible — work when and where it suits you, anywhere in the world
• Freelance autonomy with the structure of meaningful, technically substantive work
• Engage hands-on with industry-leading large language models at the cutting edge of AI development
• Potential for ongoing contract renewals as new projects launch